According to Forbes, 88% of business leaders say their workforce needs new technology upskilling within the next 12 months based on an S&P Global survey from August. The market is flooded with options—there are 3,415 AI classes on Coursera alone as of November 2025. Experts like Mariena Quintanilla from Mellonhead emphasize that success depends on business impact rather than just usage metrics. Pari Katyal at Bola AI looks for content that’s actionable and drives real differences in quality and decision-making. The focus is shifting from generic AI knowledge to specific, measurable outcomes that align with company goals.
Action Over Theory
Here’s the thing about AI training—it’s useless if people can’t apply it immediately. Helen Kupp from Women Defining AI focuses on “doing the work rather than lecturing,” which is exactly what companies need. But it’s not just about hands-on activities—it’s about building what experts call “AI muscle memory” through repeated, relevant practice. Edwin Trebels at LangOptima warns against “LinkedIn-only AI experts” and recommends working with technical trainers who actually use the tools themselves. Basically, if your team can’t connect the training to their daily workflows within the first week, you’re probably wasting your money.
Tools and Access
Mike Russo from Auction.com hits on a crucial point that many leaders miss: “Will that person have the tools to apply when they learned to produce an outcome or will they be blocked by lack of tooling?” It sounds obvious, but how many companies send people to training without ensuring they’ll have access to the actual AI tools afterward? This is especially critical in industrial and manufacturing settings where specialized hardware integration matters. Companies that get this right see immediate application—think about operations that need reliable industrial computing solutions where downtime isn’t an option. The training has to connect directly to the tools they’ll use daily.
Measuring What Matters
So how do you know if the training actually worked? Trebels’ team saw concrete results—they automated processes and saved 40-60 hours per week through better machine translation and AI-powered transcription. That’s the kind of ROI that justifies the investment. But here’s the catch: you need to measure from day one. Set baseline metrics for time spent on repetitive tasks, error rates, or whatever matters for your team’s workflows. The training should evolve into new workflows rather than being a one-off event. If you’re not seeing measurable changes within a few months, something’s wrong with either the training or how you’re implementing it.
The Expertise Gap
With thousands of courses available, the real challenge isn’t finding AI training—it’s finding the right training. The market is flooded with self-proclaimed experts who’ve never actually implemented AI in production environments. Quintanilla notes that “technical skills alone don’t lead to adoption,” which is why role-specific application matters so much. Companies that succeed with AI upskilling are the ones who treat it as an ongoing process rather than a checkbox exercise. They build continuous learning into workflows and measure everything. After all, what’s the point of training if it doesn’t change how work actually gets done?
